from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a    :	Union[str, Any]         =    logging.get_logger(__name__)

__a    :	Any         =    {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					List[Any]     					=		'''data2vec-text'''





			def __init__(  self ,  lowerCAmelCase__=3_05_22 ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=5_12 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-12 ,  lowerCAmelCase__=1 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__="absolute" ,  lowerCAmelCase__=True ,  lowerCAmelCase__=None ,  **lowerCAmelCase__ ,  )	->   List[Any]:

							'''simple docstring'''


							super().__init__(pad_token_id=lowerCAmelCase__ ,  bos_token_id=lowerCAmelCase__ ,  eos_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__		)

							__lowercase					     =				vocab_size
							__lowercase					     =				hidden_size
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				num_attention_heads
							__lowercase					     =				hidden_act
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_dropout_prob
							__lowercase					     =				attention_probs_dropout_prob
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				type_vocab_size
							__lowercase					     =				initializer_range
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				position_embedding_type
							__lowercase					     =				use_cache
							__lowercase					     =				classifier_dropout




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Mapping[str, Mapping[int, str]]:

							'''simple docstring'''


							if self.task == "multiple-choice":
											__lowercase					     =				{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
											__lowercase					     =				{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]		)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a    :	Tuple         =    {
    """configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
    """tokenization_luke""": ["""LukeTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a    :	Dict         =    [
		    """LUKE_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """LukeForEntityClassification""",
		    """LukeForEntityPairClassification""",
		    """LukeForEntitySpanClassification""",
		    """LukeForMultipleChoice""",
		    """LukeForQuestionAnswering""",
		    """LukeForSequenceClassification""",
		    """LukeForTokenClassification""",
		    """LukeForMaskedLM""",
		    """LukeModel""",
		    """LukePreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		__a    :	Any         =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)